Mitigation of End-Flux-Peaking in Fresh CANDU Fuel Bundles Using Neutron Absorbers
Bibliographic record
Abstract
Abstract End-flux-peaking (EFP) is a phenomenon where a region of elevated neutron flux occurs between two adjoining fuel bundles, leading to an increase in fission rate and therefore greater heat generation. It is known that the addition of neutron absorbers into fuel bundles can mitigate EFP, yet the implementation in Canada deuterium uranium (CANDU) type reactors using natural uranium fuel has not been pursued. The computer code Monte Carlo N-Particle code (MCNP) 6.1 was used to develop a three-dimensional CANDU bundle–bundle contact model and simulate the addition of neutron absorbers positioned strategically within various locations of the fuel bundle. The burnable absorbers of interest include Gd2O3 and Eu2O3. The locations investigated include within the end pellets of a fuel stack, within the CANDU lubricant (CANLUB) layer, within thin disks located at the ends of the fuel stack, and alloyed in the endplate. Concentrations of the absorbers were varied to gain better insight into their effect on the thermal neutron axial flux profile of the fuel bundle. The results of the study indicated that adding a combination of ∼4 mg/∼12 mg of Eu2O3 into the pellet adjacent to the end pellet and the end pellet, respectively, at each end of all six of the fuel elements in the inner fuel ring, as well as, ∼2 mg/∼6 mg of Eu2O3 into the same respective pellets, at each end of the 18 fuel elements in the outer fuel ring, provides the most effective mitigation of the EFP phenomena in fresh CANDU fuel bundles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".